Responsible AI: Implementing Ethical and Unbiased Algorithms

Foreword
Preface
	Who is this Book for?
	How to Read this Book
	How to Access the Code
Acknowledgements
Contents
Chapter 1: Introduction
	What Is Responsible AI
	Facets of Responsible AI
		Fair AI
		Explainable AI
		Accountable AI
		Data and Model Privacy
	Bibliography
Chapter 2: Fairness and Proxy Features
	Introduction
	Key Parameters
	Confusion Matrix
	Common Accuracy Metrics
	Fairness and Fairness Metrics
		Fairness Metrics
			Equal Opportunity
			Predictive Equality
			Equalized Odds
			Predictive Parity
			Demographic Parity
			Average Odds Difference
		Python Implementation
	Proxy Features
	Methods to Detect Proxy Features
		Linear Regression
		Variance Inflation Factor (VIF)
		Linear Association Method Using Variance
		Cosine Similarity/Distance Method
		Mutual Information
	Conclusion
	Bibliography
Chapter 3: Bias in Data
	Introduction
	Statistical Parity Difference
	Disparate Impact
	When the Y Is Continuous and S Is Binary
	When the Y Is Binary and S Is Continuous
	Conclusion
		Key Takeaways for the Product Owner
		Key Takeaways for the Business Analysts/SMEs
		Key Takeaways for the Data Scientists
	Bibliography
Chapter 4: Explainability
	Introduction
	Feature Explanation
		Information Value Plots
		Partial Dependency Plots
		Accumulated Local Effects
		Sensitivity Analysis
	Model Explanation
		Split and Compare Quantiles
		Global Explanation
		Local Explanation
		Morris Sensitivity
	Explainable Models
		Generalized Additive Models (GAM)
	Counterfactual Explanation
	Conclusion
	Bibliography
Chapter 5: Remove Bias from ML Model
	Introduction
	Reweighting the Data
		Calculating Weights
		Implementing Weights in ML Model
			Protected Feature: Married
			Protected Feature: Single
			Protected Feature: Divorced
			Protected Feature: Number of Dependants Less than Three
			Protected Feature: Work Experience Less than 10 Years
		Calibrating Decision Boundary
		Composite Feature
	Additive Counterfactual Fairness
		High Level Steps for Implementing ACF Model
		ACF for Classification Problems
		ACF for Continuous Output
			Linear Regression Model
			ACF Model
	Calculating Unfairness
	Conclusion
	Bibliography
Chapter 6: Remove Bias from ML Output
	Introduction
	Reject Option Classifier
		Optimizing the ROC
		Handling Multiple Features in ROC
	Conclusion
	Bibliography
Chapter 7: Accountability in AI
	Introduction
	Data Drift
		Covariate Drift
		Jensen-Shannon Distance
		Wasserstein Distance
		Stability Index
	Concept Drift
		Kolmogorov–Smirnov Test
		Brier Score
		Page-Hinkley Test (PHT)
		Early Drift Detection Method
		Hierarchical Linear Four Rate (HLFR)
	Conclusion
	Bibliography
Chapter 8: Data and Model Privacy
	Introduction
	Basic Techniques
		Hashing
		K-Anonymity, L-Diversity and T-Closeness
	Differential Privacy
	Privacy Using Exponential Mechanism
	Differentially Private ML Algorithms
	Federated Learning
	Conclusion
	Bibliography
Chapter 9: Conclusion
	Responsible AI Lifecycle
	Responsible AI Canvas
	AI and Sustainability
	Need for an AI Regulator
	Bibliography